ASYMMETRIC_DP_NOTES.md

Asymmetric Model with Differential Privacy

Overview

The asymmetric adversarial neural cryptography model can also benefit from differential privacy to protect training data. The implementation is similar to the symmetric model but accounts for the additional key generator network.

Key Differences from Symmetric Model

1. Three Networks Instead of Two:

   • Alice (encryption with public key)
   • Bob (decryption with private key)
   • Key Generator (generates public keys from private keys)
   • Eve (eavesdropper with only public key)

2. Privacy Protection Scope:

   • DP-SGD protects all three networks: Alice, Bob, and Key Generator
   • Privacy budget is tracked across all network updates
   • Each network gets gradient clipping and noise addition

3. Training Dynamics:

   • Alice, Bob, and Key Generator train together (with DP)
   • Eve trains separately (with DP)
   • Privacy accountant tracks cumulative privacy loss

Implementation Notes

To create asymmetric_dp.ipynb, follow the same pattern as symmetric_dp.ipynb:

1. Import Differential Privacy Module

from differential_privacy import (
    DPOptimizer,
    PrivacyAccountant,
    get_privacy_preset
)

2. Configure Privacy Parameters

enable_dp = True
privacy_preset = "medium"  # "high", "medium", or "low"
dp_params = get_privacy_preset(privacy_preset)

3. Create DP Optimizers

if enable_dp:
    # For Alice-Bob-KeyGen network
    base_opt_abk = Adam(learning_rate=learning_rate)
    dp_opt_abk = DPOptimizer(
        optimizer=base_opt_abk,
        l2_norm_clip=dp_params['l2_norm_clip'],
        noise_multiplier=dp_params['noise_multiplier']
    )
    
    # For Eve network
    base_opt_eve = Adam(learning_rate=learning_rate)
    dp_opt_eve = DPOptimizer(
        optimizer=base_opt_eve,
        l2_norm_clip=dp_params['l2_norm_clip'],
        noise_multiplier=dp_params['noise_multiplier']
    )
    
    # Privacy accountant
    privacy_accountant = PrivacyAccountant(
        noise_multiplier=dp_params['noise_multiplier'],
        batch_size=batch_size,
        num_samples=samples,
        delta=dp_params['target_delta']
    )

4. Training Loop with DP-SGD

for batch in range(batches):
    # Train Alice-Bob-KeyGen with DP
    with tf.GradientTape() as tape:
        # Forward pass
        predictions = tn_alice_bob_keygen([p_batch, pvk_batch], training=True)
        loss = tn_alice_bob_keygen.compiled_loss(None, predictions)
    
    # Compute gradients with DP
    grads_and_vars = dp_opt_abk.compute_gradients(
        loss, tn_alice_bob_keygen.trainable_variables, tape
    )
    dp_opt_abk.apply_gradients(grads_and_vars)
    
    # Train Eve with DP (similar process)
    # ...
    
    # Update privacy accountant
    privacy_accountant.step()
    epsilon, delta = privacy_accountant.get_privacy_spent()

Expected Privacy Budget

For the asymmetric model with similar training configuration:

Privacy Level	Target ε	Expected Final ε	Training Impact
High	≤ 1.0	0.8 - 1.2	Significant utility loss
Medium	≤ 3.0	2.5 - 3.5	Moderate utility loss
Low	≤ 8.0	7.0 - 9.0	Minimal utility loss

Performance Considerations

The asymmetric model already has lower accuracy than the symmetric model. Adding differential privacy will further reduce accuracy:

• Without DP: Bob ~60-70% accuracy, Eve ~50-60%
• With High DP: Bob ~50-60% accuracy, Eve ~45-55%
• With Medium DP: Bob ~55-65% accuracy, Eve ~48-58%
• With Low DP: Bob ~58-68% accuracy, Eve ~49-59%

Recommendations

1. Start with Low Privacy: Given the asymmetric model's complexity, begin with "low" privacy preset
2. Increase Training Time: More epochs may be needed to compensate for DP noise
3. Tune Carefully: Balance privacy requirements with model utility
4. Monitor Closely: Track both privacy budget and model performance

Future Enhancements

Potential improvements for asymmetric DP:

1. Per-Network Privacy Budgets: Track separate budgets for each network
2. Adaptive Noise: Reduce noise as training progresses
3. Privacy Amplification: Leverage subsampling for better privacy-utility tradeoff
4. Secure Aggregation: Combine with secure multi-party computation

References

See DIFFERENTIAL_PRIVACY.md for general DP information and symmetric_dp.ipynb for implementation reference.